6G networks will use artificial intelligence (AI)-based techniques for resource management, network control and monitoring. The concept of `edge AI' has been proposed recently which pushes the network intelligence at the edge devices, and enables the AI-based learning algorithms to run at distributed edge devices [1]}. The federated learning fits the edge computing architecture well due to the distributed learning model, data privacy protection function and the alleviation of communication overhead [2]}, [3]}. However, in a vehicular edge computing network, the mobility of vehicles becomes an issue in keeping continuous information sharing between the edge and the cloud server. Also, the centralized cloud server is vulnerable to security threats, which may lead to a failure of the learning model [4]}.
